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Description

We define a new “contour person” model of the human
body that has the expressive power of a detailed 3D model
and the computational benefits of a simple 2D part-based
model. The contour person (CP) model is learned from a
3D SCAPE model of the human body that captures natural
shape and pose variations; the projected contours of this
model, along with their segmentation into parts forms the
training set. The CP model factors deformations of the body
into three components: shape variation, viewpoint change
and part rotation. This latter model also incorporates a
learned non-rigid deformation model. The result is a 2D
articulated model that is compact to represent, simple to
compute with and more expressive than previous models.
We demonstrate the value of such a model in 2D pose es-
timation and segmentation. Given an initial pose from a
standard pictorial-structures method, we refine the pose and
shape using an objective function that segments the scene
into foreground and background regions. The result is a
parametric, human-specific, image segmentation.